Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Automated Advice-Giving Strategies for Scientific Inquiry
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Probabilistic Student Modelling to Improve Exploratory Behaviour
User Modeling and User-Adapted Interaction
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Looking Ahead to Select Tutorial Actions: A Decision-Theoretic Approach
International Journal of Artificial Intelligence in Education
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Students' reasoning during modeling in an inquiry learning environment
Computers in Human Behavior
A decision-theoretic approach to scientific inquiry exploratory learning environment
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Coaching within a domain independent inquiry environment
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Properties of Bayesian student model for INQPRO
Applied Intelligence
Image annotation by modeling Supporting Region Graph
Applied Intelligence
Hi-index | 0.00 |
While recent studies employ heuristic to support learners in scientific inquiry learning environments, this study examined the theoretical and practical aspects of decision-theoretic approach to simultaneous reason about learners' scientific inquiry skills and provision of adaptive pedagogical interventions across time. In this study, the dynamic learner model, represented by three different Dynamic Decision Network (DDN) models, were employed and evaluated through a three-phase empirical study. This paper discusses how insights gained and lessons learned from the evaluations of a preceding model had led to the improvements of subsequent model; before finalizing the optimal design of DDN model. The empirical studies involved six domain experts, 101 first-year university learners, and dataset from our previous research. Each learner participated in a series of activities including a pretest, a session with INQPRO learning environment, a posttest, and an interview session. For each DDN model, the predictive accuracies were computed by comparing the classifications given by the model with (a) the results obtained from the pretest, posttest, and learner self-rating scores, and (b) classifications elicited by domain experts based on the learner interaction logs and the graphs exhibited by each model.